Asset Management, GIS and LiDAR Projects

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We are all familiar with the day-to-day activities happening throughout our organization. Lots of things are happening and this information is being captured in our Asset Management System throughout the day, week month and year. Information is an asset in and of itself, but it is neglected at many organizations because of the focus on “today’s” activities. Dashboards can provide a passive way of visualizing data so that Performance metrics can be monitored (e.g. How many Issues are older than 24 hours?). It can also provide Insights into your data that are not easily seen in tabular layouts of data Filters and Reports. This information should be presented to the Asset Management user in a way that is relevant to their day-to-day activities and should be interactive, giving the user control over the information in front of them.

VUEWorks set out to create this exact user experience when we decided to overhaul our Dashboard tools. The following goals were presented to the Development team along with their associated User Stories. This process would focus on the User, so we also enlisted many key clients from our customer base to provide the guidance necessary to achieve these goals:

Provide a Unique User Experience, focused on the Individual User as well as Groups of Users who share the same, common information.

Provide a Wizard-driven User Interface (UI) to make it easy for anyone to author their own Dashboard Widgets.

Expose all Data Sources and GIS Layers to allow for ANY type of Dashboard to be created.

Provide a series of commonly used Dashboard Widget Types and share them among users within the organization.

Integrate with / Leverage Existing Filters throughout the VUEWorks interface to make it easy for users to get their specific data delivered to their Dashboard without having to re-create anything.

Provide a series of Mathematical Functions (e.g. Count, Average, Min, Max, Sum) that can be organized by Values (Good, Fair, Poor) or Ranges of Values (Bins).

Allow the user to change the Appearance (2D or 3D), Legend Settings (Marker Types, Position) or General Settings (Labels, Font and Margins) related to the Widget.

Integrate with Existing User and Role-based Security found throughout VUEWorks.

Provide easy to use Help and On-line resources to help train users and expose them to new functionality.

Pavement management incorporates data collected utilizing various methods to gain a complete view of how the pavement is performing through its life-cycle. One of the most common practices in pavement inspection is imaging utilizing high-resolution cameras mounted on vehicles outfitted with precision GPS and inertial navigation. This imaging, when combined with laser profiling, constitutes a typical pavement inspection setup utilized by many DOTs as well as Local government agencies.

Pavement Inspections tend to follow a process that in many cases is proprietary and “black box” in nature. This makes it hard for the purchasing agency to see how their roads were inspected and how the resulting pavement condition scores were generated. Our team of Engineers and GIS professionals have worked hard to develop a process to remove the “black box” related pavement inspection and to make it easy and simple to trace inspection results back to their originating distresses from the field.

First, our entire process is geospatial in nature from the get-go. Our van’s location is tracked in six-dimensions in real-time and this information is used to calculate the exact location of pavement cracks in the resulting images. Next, the pavement images are geospatially referenced in 3-d and 1mm-pixel resolution, making it easy to extract low-severity cracks in a true 3-d environment. This process then allows us to create GIS vectors (points, lines and polygons) of each distress for each pavement image and deliver them to our clients as part of the pavement inspection deliverables.

This is a crucial piece to the pavement inspection “story” because it shows the purchasing agency exactly what distresses were identified and measured when creating the pavement condition scores for a section of road. Being able to see these distresses on a map helps to complete the story by providing the ability for a rigorous QA/QC process utilizing some simple GIS tools.

Each Section of road can be colored by the condition score and its range of values. This tells one component of its story. The underlying distress information tells the rest of the story related to “How” a section of road was scored and assigned its inspection score. By having this information at their fingertips, pavement inspection personnel have a GIS-centric and user-friendly tool that allows them to QA/QC pavement inspection data efficiently.

We get a lot of questions about developing the right strategy as it relates to assets that are managed by different agencies. These questions are typically focused on “How” to manage assets, which typically comes after the agency decides “Why” to manage assets.

Here are some typical questions:

When is the best time to manage my asset in its life-cycle?

When do I rehabilitate my asset?

What do I do to the asset?

When do I replace my asset?

Can I just let it runs its course and when it fails, replace it?

Should I invest time and money in an asset early in its life-cycle or wait until it is in poor condition to fix it?

We always recommend starting this process by understanding a few things about the asset.

1. Financial Considerations – How much does an asset cost to install and Maintain? Is it capitalized or not? In most cases, the cost of an asset has a large impact on how it is managed. This is not the only consideration, but we can use it as a starting point.

2. Risk Considerations – What are the consequences to the agency if this asset fails? Will someone get hurt? Will it cause an accident? These are closely tied to other financial considerations such as tort liability.

3. Life-Cycle Considerations – How does the asset typically deteriorate? Is it straight-line deterioration or more of a polynomial-type of a curve? This information helps determine what to do to an asset and when to do it (less cost when starting earlier in the process). Programmatic treatments or inspection-driven treatments are common approaches to managing assets with this approach.

Once an agency has a solid understanding of the Financial, Risk and Life-Cycle considerations related to an asset, they can begin to develop a management strategy specifically for the asset type to be managed. Since every asset can be managed differently, we will focus on a couple of assets and their management strategy.

Pavement

Financial – Capitalized asset – high cost to install and maintain.

Risk – Critical to the movement of people and commerce – high consequence of failure.

Life-Cycle – Long-term asset with long-term life expectancy – Can be managed using a life-cycle or Inspection-based approach.

Pavements have a long history of research and empirical data models that have been developed for Airports, Parking lots and Roads and a variety of software exists to support the maintenance of this asset. Therefore, it is pretty easy to choose an approach to manage pavement based on an agency’s goals and priorities. Typically this program is inspection-driven (every 3-5 years) and focuses on finding the best mix of Preservation and Rehabilitation activities designed to achieve their target Level-of-Service.

Signs

Financial – Capitalized asset – low to high cost to install and maintain.

Risk – Critical to the safety of people and commerce – low to high consequences of failure.

Life-Cycle – Medium to long-term life-expectancy – Can be managed using a life-cycle or Inspection-based approach.

Signs have less empirical data collected for them and can have varied Financial, Risk and Life-cycle information compiled and available throughout the industry. Strategies for management are typically focused on Life-Cycle and Risk and there are many methodologies that are accepted by FHWA. These are outlined in their Manual on Uniform Traffic Control Devices (MUTCD) and are widely utilized throughout the US.

Light Poles

Financial – Capitalized asset – medium cost to install and maintain.

Risk – Semi-Critical to the safety of people and commerce – low to high consequences of failure.

Life-Cycle – Medium to long-term life-expectancy – Can be managed using a life-cycle or Inspection-based approach.

Light poles are typically managed by inspection of their base attachments (every 10 years or so) but many agencies typically run these assets to failure (luminaire failure or pole failure). This is another mixed bag of management because some light poles provide a critical safety function (DOT) and others just light the way for safety (walkways) and are not as critical to the daily operations of an agency.

These are just a few examples of strategy development – we would love to see comments related to the infrastructure that you manage and we will reply with some of the Industry’s Best-Management-Practices (BMPs) that are successfully used throughout the US.

This article was originally written in 2011, but is being re-posted based on recent events…

DTS/Earth Eye just completed a positive train control (PTC) project for a national train company who was evaluating the differences between Airborne LiDAR and Mobile LiDAR to support the collection of PTC data. They are currently collecting airborne data for approximately 15,000 linear miles of rail. In certain areas, the airborne data does not provide enough fidelity to accurately map the rails or the asset infrastructure that support the railroad operations.

From Wikipedia – “The main concept in PTC (as defined for North American Class I freight railroads) is that the train receives information about its location and where it is allowed to safely travel, also known as movement authorities. Equipment on board the train then enforces this, preventing unsafe movement. PTC systems will work in either dark territory or signaled territory and often use GPS navigation to track train movements. The Federal Railroad Administration has listed among its goals, “To deploy the Nationwide Differential Global Positioning System (NDGPS) as a nationwide, uniform, and continuous positioning system, suitable for train control.”

The project involved the collection of Mobile LiDAR using the Riegl VMX-250 as well as forward-facing video to support PTC Asset Extraction. The system was mounted on a Hi-Rail vehicle and track access was coordinated through the master scheduler with the Railroad company. Once we had access to the tracks, we had one shot to make sure the data was collected accurately and we had complete coverage. All data was processed on-site to verify coverage and we had a preliminary solution by the end of the day that was checked against control to verify absolute accuracies. We collected the 10-mile section of rail in about 2 hours and this timing included a couple of track dismounts required to let some freight trains move on through.

The following graphics illustrate the point cloud coverage colored by elevation (left) and Intensity (right).

Mapping the rails in 3D was accomplished by developing a software routine designed to track the top of the rail and minimize any “jumping” that can occur in the noise of the LiDAR data. Basically, a linear smoothing algorithm is applied to the rail breakline and once it is digitized the algorithm fits it to the top of the rail. The following graphic illustrates how this is accomplished – the white cross-hairs on the top of the rail correspond to the breakline location in 3D.

So, back to the discussion about Airborne PTC vs Mobile PTC data. Here is a signal tower collected by Airborne LiDAR. The level of detail needed to map and code the Asset feature is lacking, making it difficult to collect PTC information efficiently without supplemental information.

The next graphic shows the detail of the same Asset feature from the mobile LiDAR data. It is much easier to identify the Asset feature and Type from the point cloud. In addition to placing locations for the Asset feature, we also provided some attribute information that was augmented by the Right-of-Way camera imagery. By utilizing this data fusion technique, we can provide the rail company with an accurate and comprehensive PTC database.

This graphic shows how the assets are placed in 3D, preserving the geospatial nature of the data in 3D which is helpful when determining the hierarchy of Assets that share the same structure.

One last cool shot of a station with all of the furniture, structures, etc that make it up – pretty cool!

We do business with a lot clients these days who are looking for an “Enterprise Asset Management” system . They use this term during the procurement process, but in a lot of cases their requirements are centered on Work Management and barely scratch the surface of Asset Management. This is easy to do since most of an organization’s daily activities are focused solely on today’s maintenance of their Asset Infrastructure, but there is very little focus on how they will manage and maintain assets into the future. Our clients are always answering questions related to the fiscal activities centered on asset performance. The questions from management are centered around:

How much are we spending on maintenance?

How long does it take us to respond to and fix an issue?

Are we meeting Federally mandated requirements?

Anything else relating to money…

The IAM defines asset management as the “coordinated activity of an organization to realize value from assets”. This involves the “balancing of costs, opportunities and risks against the desired performance of assets, to achieve the organizational objectives.” An additional objective is to “minimize the whole-life cost of assets but there may be other critical factors such as risk or business continuity to be considered objectively in this decision making.” All of these factors can be combined together to make informed decisions regarding how assets are managed and maintained throughout their life-cycle. These decisions involve monetary expenditures, but they also involve strategic thinking centered on the “How” and “Why” to fix an asset as well as “When” and “Which” portions of this process. This is the “Strategic” piece of an Asset Management system.

Work Management is one small component of Asset Management. It is typically focused on the day-to-day operations and expenditures related to operating and maintaining asset infrastructure. The Work done against an asset can track cost information, but can also be used to build a strategy around the operations and maintenance related to that asset. This strategy focuses on the “How” and “Why”. It answers what “Activity” should be completed for an asset (Install, Maintain, Repair, Replace) and “Why” (It’s old, looks bad, is dangerous, could cause injury, get us sued) this should happen. Next, it answers “When” (now, next year, or never) an asset should be maintained as well as “Which” (most critical, most likely to fail, the Mayor’s sewer line) assets should get priority. All of these factors are important and ALL of them should be utilized when making a Strategic Asset Management decision. Be reminded that Work Management is only one component of this decision-making criteria which is applied to an overall Strategic Asset Management plan.

DOTs across the Country are mandated by the Federal Government to keep track of their roadway assets and to report against these assets to receive Federal funding for their maintenance and repair. Many DOTs conduct Roadway Characteristics Inventories (RCI) on an annual basis to update and maintain their data relative to these assets. Traditionally, this has been completed using a boots-on-the-ground approach which has been very effective at building these inventories. Many DOTs are experimenting with other technologies, namely mobile LiDAR, to conduct these inventories and to achieve many other benefits from the 3D data captured in the process.

The next graphic illustrates the typical technology solution utilized for these projects. It is composed of the Riegl VMX-450 LiDAR unit, coupled with High-definition Right-of-Way (ROW) imagery. This system can collect at rates up to 1.1 KHz (1,100,000 pts/sec) at a precision of 5mm. It collects points in a circular (360-degree) pattern along the right-of-way from 2 scanner heads facing forward and to the rear of the vehicle in a crossing pattern. The laser captures 3D points at a density of 0.3 foot at speeds up to 70mph. This scanner can be adjusted to scan at a rate that is applicable for the project specifications to limit the amount of data collected and to ensure that the resulting point cloud data is manageable.

Right-of-Way imagery is also co-collected along with this LiDAR point cloud data. These images are used to identify appropriate attribution for each feature type being extracted from the point cloud. In this example, the DOT has digitized Shoulder, Driveway Culvert Ends, and Drainage Features (Culverts, Ditches and Bottom of Swale). Additional Features such as Signs, Signals, Striping, and Markings will also be extracted and then reported to the Feds on an annual basis. The mobile LiDAR data provides a 3D surface from which to compile the data and then the ROW imagery can be used for contextual purposes to support attribution. This methodology provides an effective process that can be used to create 3D vector layers and accurate attribution used to build a robust Enterprise GIS.

Both the ROW imagery and the mobile LiDAR can be used to collect and extract the RCI data efficiently for the DOTs and provides the DOT with a robust data set that can be leveraged into the future. The ROW imagery is typically used to map features at a mapping-grade level while the LiDAR can vary a bit in accuracy. Since the relative accuracy inherent in the LiDAR is very precise, it is used to conduct dimensional measurements related to clearances, sign panel sizes, lane widths, and other measurements that require a higher precision.

The DOT utilizes the derivative products from this RCI exercise to report to the Feds in a way that is pretty basic, but effective to achieve their level of funding. For example, the data capture is very technical in nature and focuses on high precision and accuracy. Then, the RCI data is extracted from this source data, maintaining a level of precision that is dictated by the source data. Then, the DOT takes this precise data and aggregates it up to a higher level and reports the total number of Signs or the lineal feet of guardrail. Even though the reporting of this data is pretty basic in nature, the origins of the data can still have precision and accuracy and can be used for other purposes related to Engineering Design or Asset Management.

In conclusion, mobile LiDAR and Right-of-Way imagery are a safe and accurate way to collect and report against RCI variables for DOTs. This methodology promotes a safe working environment for both the DOT worker and the traveling public. It is also a cost-effective way to collect large amounts of 3D point cloud data which can be utilized for other purposes within the same Agency.

Many utilities collect their infrastructure inspection data using a variety of techniques, sources and systems of record. Having many different repositories of digital information makes it difficult to make informed decisions about where to spend operations and maintenance (O & M) and capital project dollars. Having a “crystal ball” that aggregates all of this data into one single user interface could help these utilities make more informed decisions for their infrastructure as a whole, instead of using one inspection type to make these decisions.

For example, utilities typically collect information related to their structures and spans using one or a combination of these inspection techniques:

Patrols

Corona

Infrared Inspections

Climbing Inspections

Walking Inspections

Vegetation Points-of-Interest (LiDAR and Visual) Inspections

NERC encroachments (LiDAR) Inspections

Comprehensive Visual Inspection (CVI)

All of these inspections generate a large amount of data independent of one another and can be very useful if combined based on a unique structure or span number. Once combined, this information can then be used to determine the best way to bundle work activities to achieve the greatest return-on-investment (ROI).

Work bundling is a concept that has been well understood in the utility industry but not commonly practiced due to the disparate ways in which inspection data is collected and accessed from within a single agency. Many work management systems only focus on the recording of work order information related to the labor, equipment and materials used to perform a project, but do not contain strategic planning tools. These tools allow an agency to conduct “what-if” scenarios by applying different budget amounts against a planned work matrix.

Once the optimal work matrix is determined, a workplan for that utility can then be planned and programmed, executed and tracked as a project or a series of projects for that planning horizon. All costs related to that work matrix can be applied to each asset and tracked against an overall workplan budget. These actual costs are then compared to the estimated costs to refine the planning matrix unit costs that are feeding the budget forecasting model.

As an agency completes the work for that particular period, it can then record the work activities against a particular asset which determines its next activity that is due in its life-cycle. As this feedback loop is established, more cyclical work can be planned and programmed for future fiscal years and budget plans.

This concept has been applied at many utilities through the US using an asset management software called VUEWorks. This software is GIS-centric at its core and allows users to connect their GIS data to their asset management system through the use of Esri GIS software. The utility creates a map service which is consumed by VUEWorks and provides a mapping framework from which users can view inspection data from various sources.

For example, a helicopter inspection company collects CVI data by flying next to the transmission structures and collects high-resolution imagery of any defects located on that structure or its associated span. Another vendor collects walking inspection information which includes subterranean excavations around a structure and its supports. These inspections yield different defects which may require different types of activities to correct them. This is where the concept of work bundling can be used.

Since each inspection yielded different defects, the structure or span will need to be worked on at some point. It is important that all departments responsible for line maintenance understand all of the defects present on a particular structure or span so that they can conduct all work activities at the same time. In essence, VUEWorks provides this exact information, all in one place. The utility has the ability to link all of this data together based on a structure or span ID and can then view all inspection data from one single user interface.

This concept is important because if a utility needs to de-energize a line for maintenance or capital improvements, it will want to ensure that all issues are resolved during one outage. Multiple outages cost money and this concept of work bundling is helping utilities achieve high ROIs for these projects by combining projects into one single project, instead of multiple projects.

In conclusion, the concept of work bundling saves utilities time and money through the aggregation of data into a single user repository. This information can easily and effectively be used to make informed decisions and avoid multiple outage situations. By combining multiple inspection data sets together, utilities can more proactively manage their assets cost-effectively while extending the useful life of their infrastructure investment.